Search Results (376)

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Traffic flow prediction plays an important role in intelligent transportation systems. However, accurately modeling traffic dynamics remains challenging due to complex temporal correlations and spatial interactions across road networks. In this work, we propose a Mamba-based macro–micro spatio-temporal model for traffic flow prediction. Unlike graph-based approaches that rely on predefined adjacency matrices to model spatial relationships, our method treats sensor nodes as sequence elements and applies Mamba blocks along the spatial dimension. Through the global receptive field of the structured state space model, spatial dependencies are implicitly learned without requiring explicit graph structures. The proposed architecture consists of stacked spatio-temporal blocks, each composed of two Macro Feature Blocks and one Micro Feature Block. The Macro Feature Blocks are designed to capture global temporal dependencies and spatial interactions across all nodes, while the Micro Feature Block focuses on modeling localized spatio-temporal patterns at a finer granularity. By applying structured state space modeling along both temporal and spatial dimensions, the model is able to capture long-range temporal dependencies and global spatial correlations without relying on explicit graph structures. Experiments conducted on four real-world datasets demonstrate that the proposed model achieves competitive or improved performance compared with existing baseline methods under standard evaluation metrics. Full article

Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments—referred to as the “cold-start” problem—remains a critical bottleneck, frequently leading to overfitting and severe error accumulation on ultra-long trajectories. To surmount these limitations, this study proposes the Dual-Attentional Gated Residual Network (DAGRN), a data-efficient forecasting framework driven by a novel topology-temporal coordination mechanism. Specifically, the framework introduces three integrated innovations: (1) transforming the primal network into a physics-aware Line Graph to explicitly filter out illegal movements and dynamically modulating topological propagation via Feature-wise Linear Modulation (FiLM); (2) coupling a Bidirectional GRU backbone with a Multi-Head Attention module to simultaneously capture global trends and localized intersection delays; (3) employing a Gated Residual Fusion mechanism that preserves dimensional consistency and facilitates gradient flow in extensive sequences. To rigorously validate the model’s robustness, we conduct evaluations on a highly constrained, stratified dataset comprising merely 2000 trajectories. Experimental results demonstrate that DAGRN achieves state-of-the-art predictive precision with an RMSE of 415.485 s and an R² of 0.848, significantly outperforming 12 advanced baseline models and reducing error by up to 13.8% against the strongest graph baseline. Comprehensive ablation studies confirm the absolute necessity of the Multi-Head Attention module, whose removal causes the most severe performance degradation (RMSE surging to 521.495 s). Ultimately, DAGRN presents a readily deployable solution for sparse-data ITS regimes, actively paving the way for future hybrid integrations with microscopic traffic simulations and evolutionary road network optimization algorithms. Full article

Autonomous navigation in complex environments requires efficient and reliable road-network representations for fast path planning. However, traditional grid and skeleton-based approaches often suffer from high computational cost and limited path quality. This paper proposes a Hierarchical Topology-Metric Road Graph (HTMRG) framework for autonomous navigation of unmanned ground vehicles (UGVs). The method automatically constructs a hierarchical road graph from grid maps by identifying key intersection structures and generating smooth corridor and intersection connections. In addition, a dedicated start–goal insertion strategy is developed to enable efficient graph-based path planning in previously unexplored scenarios. Extensive simulations and real-world experiments demonstrate that the proposed method can automatically construct hierarchical road graphs and generate smooth, high-quality paths with improved planning efficiency and robustness. The HTMRG framework has also been successfully integrated into a UGV system, validating its effectiveness and practicality in real-world navigation scenarios. Full article

The integration of heterogeneous geospatial data, specifically low-cost unmanned aerial vehicle (UAV) imagery and mobile light detection and ranging (LiDAR) system point clouds, presents a significant challenge due to the significant radiometric and structural discrepancies between the two modalities. This study proposes a novel air-to-ground semantic feature matching framework to achieve precise geometric registration between these data sources by effectively incorporating semantic-constraint deep learning-based matching. The methodology transformed the cross-sensor alignment challenge into a robust two-dimensional image matching problem. This was achieved by first using YOLOv11 for semantic segmentation of common road markings in both the UAV orthoimage and the converted LiDAR intensity image to generate highly consistent feature references. Subsequently, the SuperPoint detector and a graph neural network matcher, SuperGlue, were applied to these semantic images to establish reliable geomatics information correspondence points. Experimental results confirmed that this semantic-guided strategy consistently outperformed traditional feature-based matching (i.e., scale-invariant feature transform + fast library for approximate nearest neighbors), particularly by converting the noisy LiDAR intensity image into a stabilized semantic representation. The explicit application of semantic constraints further proved effective in eliminating false matches between geometrically similar but semantically distinct objects. The final object-specific analysis demonstrated that features with clear, complex geometric structures (e.g., pedestrian crossings and directional arrows) provide the most robust matching control. In summary, the proposed framework successfully leverages semantic context to overcome cross-sensor heterogeneity, offering an automated and precise solution for the geometric alignment of mobile LiDAR data. Full article

Urban road networks (URNs) underpin critical urban functions ranging from public service provision to emergency response. However, URN resilience is commonly assessed using aggregate performance metrics or critical-element identification, which offers limited insight into how disruption reshapes spatial accessibility. This limitation is increasingly salient under stock-based urban development, where opportunities for large-scale physical network reconfiguration and segment-level engineering interventions are constrained, and resilience enhancement increasingly depends on facility-based adaptation. To address this gap, drawing on graph theory and percolation theory, this study proposes a disconnection-pattern-based (DPB) analytical approach for mapping spatial configurations of URN vulnerability. Two generic disconnection patterns derived from topological limits of network redundancy are conceptualized: Local Island Disconnection (LID) and Global Structural Fragmentation (GSF). Corresponding quantitative mapping methods are developed and applied to cities with contrasting URN morphologies. Results show that spatial configurations of connectivity vulnerability can be systematically mapped across heterogeneous URNs, yielding spatially explicit information critical to resilience-oriented facility siting. By treating vulnerability as a spatial configuration rather than a single-state metric, the proposed approach extends URN resilience assessment toward facility-planning strategies that adapt to existing road-network risk configurations under stock-based development. Full article

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, supporting traffic management, congestion mitigation, and efficient utilization of road resources. Advances in neural network-based methods, particularly graph neural networks (GNNs) and attention-based models, have demonstrated strong capability in modeling spatio-temporal traffic dynamics. However, existing approaches still face notable challenges: GNN-based models often rely on static adjacency matrices, limiting their ability to capture dynamic and long-range spatial dependencies, while attention-based models usually involve complex architectures and heavy reliance on large-scale pre-training data. To address these limitations, this study proposes a novel traffic flow prediction model that integrates a learnable memory tensor into an attention-based framework. The introduced memory mechanism provides persistent global context for modeling long-term temporal dependencies in an end-to-end manner, enabling efficient and dynamic spatio-temporal representation learning with a lightweight architecture. Extensive experiments on multiple real-world traffic datasets demonstrate that the proposed model achieves superior prediction accuracy and robustness compared with existing baselines. The proposed approach offers a new perspective for memory-enhanced spatio-temporal modeling and provides valuable insights for traffic forecasting and related intelligent transportation applications. Full article

Updating road network information requires determining an optimal path that visits all roads requiring data collection. This problem, known as the Rural Postman Problem (RPP), is traditionally addressed using relational databases. However, graph databases may offer advantages by more naturally representing network structures, where nodes represent junctions and edges represent roads. This study explores the novel representation of road networks using graph databases and its unique application in optimizing RPP algorithms. We implemented three existing algorithms—(a) Nearest Neighbor, (b) “Biased” Monte Carlo, and (c) Genetic algorithm—using Cypher and compared them with (d) a novel Cypher-only algorithm designed to compute the optimal path. The results show that the Nearest Neighbor algorithm was the fastest and produced the shortest paths among all algorithms. The Cypher-only algorithm could also identify optimal paths but failed to scale beyond five required edges. These findings highlight the limitations of using Cypher alone for solving the RPP but suggest that Neo4j and Cypher hold promise for further exploration. Full article

In recent years, people have increasingly sought to generate exercise trajectories that embody specific semantic shapes in order to create GPS art and share it on social platforms. This trend has created an urgent demand for navigation paths with specific semantic meanings on smartwatches and smartphones. Current methods mainly rely on manual design and lack efficient automation. Therefore, this study proposes a novel method for automatically obtaining navigation paths with specified shapes by retrieving graphics similar to the input graphic shape from the road network. This method uses invariant spatial relationships, such as turning angles and length ratios, along with graph matching techniques to establish one-to-one or one-to-many correspondences between line segments in the input individual graphics and those in the road network. This enables the retrieval of individual graphics within the road network. Based on this, a greedy strategy-based algorithm is proposed to solve the combined graphics retrieval problem. The results are evaluated to ensure high quality. The accuracy and effectiveness of our method are validated through experimental results using simulated and real road network data from five different regions. Furthermore, shape-constrained graphics retrieval expands the application domain of spatial scene matching. Full article

City-scale forecasting is essential for both electric-vehicle (EV) charging operations (e.g., peak management and resource allocation) and urban mobility management (e.g., road-network flow monitoring and incident response). Spatio-temporal graph neural networks (STGNNs) are a natural candidate for these problems, yet their performance often critically depends on the choice of a predefined graph prior and the availability/quality of exogenous signals. Importantly, we do not intentionally construct a poor graph; rather, we treat any predefined adjacency as a testable hypothesis and verify its alignment with the forecasting target via no-graph ablations and lightweight diagnostics ($\Delta corr$, $E_D$). In this work, we present a unified experimental pipeline based on a spatio-temporal graph convolutional network (STGCN) backbone and conduct systematic ablations on (i) whether and how a predefined static graph is used and (ii) how feature sets influence multi-step forecasting accuracy. We evaluate on two city-scale hourly datasets with heterogeneous node counts (UrbanEV: 275 nodes; CHARGED-AMS_remove_zero: 1388 nodes) and a 24 h input/6 h output setting. Across datasets, we find that a static graph can be beneficial only when it matches the true dependency structure; otherwise, it may degrade accuracy substantially. On UrbanEV, removing the graph component improves overall MAE from 116.21 ± 5.43 to 66.53 ± 1.71 (S = 5 seeds, 0–4), outperforming a persistence baseline (MAE 94.16). Feature ablations further analyze how occupancy and price signals affect UrbanEV accuracy (e.g., MAE 87.32 with all features under the evaluated feature setting). On CHARGED, the volume-only setting performs best among tested feature combinations (MAE 0.127), closely tracking a persistence baseline (MAE 0.139), while additional covariates may introduce noise under static modeling. We provide detailed multi-horizon results and discuss practical implications for when graph priors help or hurt in real deployments. Full article

To address the rapidly growing demand for charging stations (CSs) and the associated challenges posed by the expansion of electric vehicles (EVs), this study proposes a collaborative planning method integrates user demand considerations with operational constraints at the grid level. Based on graph theoretical principles, static topology models of the road network and distribution grid were constructed. A dynamic origin–destination (OD) prediction framework was then formulated by jointly considering traffic flow variations, battery energy consumption, user charging behavior, and ambient temperature, in which an enhanced gravity model is coupled with the Floyd algorithm. Charging load characteristics were quantified through Monte Carlo simulation, and K-means++ clustering was further applied to identify spatial charging demand hotspots. On this basis, a multi-objective optimization model was established to simultaneously balance the annualized cost of charging stations, user costs, and voltage deviation in the distribution network. To solve the resulting high dimensional problem, a collaborative optimization mechanism was designed by integrating a weighted Voronoi diagram with a multi-objective particle swarm optimization (MOPSO) algorithm, enabling dynamic service area partitioning and global capacity optimization. Case analysis demonstrates that the proposed method reduces user time costs by 15.8%, optimizes queue delay by 42.2%, and improves voltage stability, maintaining fluctuations within 5%. It also balances the interests of charging station operators, users, and distribution networks, with only a slight increase in construction costs. These results offer valuable theoretical and practical insights for charging infrastructure planning. Full article

Traffic flow prediction is a pivotal task in intelligent transportation systems. The primary challenge lies in accurately modeling the dynamically evolving and directional spatio-temporal dependencies inherent in road networks. Existing graph neural network-based methods suffer from three main limitations: (1) symmetric adjacency matrices fail to capture the causal propagation of traffic flow from upstream to downstream; (2) the serial combination of graph and temporal convolutions lacks an explicit modeling of joint spatio-temporal state transition probabilities; (3) the inherent low-pass filtering property of temporal convolutional networks tends to smooth high-frequency abrupt signals, thereby weakening responsiveness to sudden events. To address these issues, this paper proposes a causal-enhanced spatio-temporal Markov graph convolutional network (CSHGCN). At the spatial modeling level, we construct an asymmetric causal adjacency matrix by decoupling source and target node embeddings to learn directional traffic flow influences. At the spatio-temporal joint modeling level, we design a spatio-temporal Markov transition module (STMTM) based on spatio-temporal Markov chain theory, which explicitly learns conditional transition patterns through temporal dependency encoders, spatial dependency encoders, and a joint transition network. At the temporal modeling level, we introduce differential feature enhancement and high-frequency residual compensation mechanisms to preserve key abrupt change information through frequency-domain complementarity. Experiments on four datasets—PEMS03, PEMS04, PEMS07, and PEMS08—demonstrate that CSHGCN outperforms existing baselines in terms of MAE, RMSE, and MAPE, with ablation studies validating the effectiveness of each module. Full article

Understanding the propagation interactions among intersections in city road networks and uncovering their traceability patterns is vital for proactive traffic management and control. However, measuring the propagation strength between intersections is difficult due to the dynamic nature of traffic flow and the interference at the network level caused by interactions among many nearby intersections. Additionally, mining traceability patterns requires a comprehensive representation of complex propagation influences among intersections and the ability to detect subtle changes in network structure. This study introduces a detailed framework for extracting traceability patterns in urban road networks. It identifies high-impact intersections using the mean excess function, constructs an interaction graph with these critical nodes, applies graph structural entropy to describe the global topological features of the interaction graph, and uses k-means clustering to classify different traceability patterns. The proposed method was validated using real-world traffic data, showing superior performance in estimating propagation strength compared to benchmark models. Kolmogorov–Smirnov tests confirmed the statistical reliability of high-impact and high-impact intersection identification results. Furthermore, the study identified four core interaction structures—chain, collider, fork, and circle—and four representative traceability patterns formed by these structures. Full article

Wireless communication and the Internet of Things (IoT) are integrated for the formulation of an emerging Intelligent Transportation System (ITS) for the interaction of vehicles and to enhance road safety. The emerging network manages the traffic flow, real-time data analytics, and resource control for the development of urban transportation systems and smart cities. Extensive research has been conducted on the development of efficient routing response time for the IoT-ITS environment; however, the rapid changes in the network topologies still lead to unmanageable congestion and communication holes. Moreover, it is also often threatened due to high urban mobility and incurs additional transmission with excessive overhead. Such concepts are not able to maintain secure interactions among vehicles and expose confidential data to malicious devices while interacting on unpredictable channels. This research proposes a trust-aware edge-assisted model to secure the vehicular network and offers a more reliable system with optimal routing performance. The global trust model is maintained based on network conditions using localized computing and attaining data privacy and coherence. Furthermore, a blockchain ledger is included along with trust to ensure tamper-proof and transparent computing across the boundaries of the IoT-ITS environment. The proposed model is compared with Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA), and the results revealed significant performance for network throughput by 50% and 62.5%, end-to-end delay by 33.3% and 37.5%, routing overhead by 34% and 38.7%, and false positive rate by 67.9% and 68.5% over the dynamic network infrastructure. Full article

This paper presents a novel deep learning framework for traffic crash prediction that leverages graph-based representations to model complex interactions among road users. At its core is a dynamic Graph Attention Network (GAT), which abstracts road users and their interactions as evolving nodes and edges in a spatiotemporal graph. Each node represents an individual road user, characterized by its state as features, such as location and velocity. A node-wise Long Short-Term Memory (LSTM) network is employed to capture the temporal evolution of these features. Edges are dynamically constructed based on spatial and temporal proximity, existing only when distance and time thresholds are met for modeling interaction relevance. The GAT learns attention-weighted representations of these dynamic interactions, which are subsequently used by a classifier to predict the risk of a crash. Experimental results demonstrate that the proposed GAT-based method achieves 86.1% prediction accuracy, highlighting its effectiveness for proactive collision risk assessment and its potential to inform real-time warning systems and preventive safety interventions. Full article